Flipboard

DECEMBER 11, 2024

Data and AI governance Publish your data products to the catalog with glossaries and metadata forms. With SageMaker Unified Studio notebooks, you can use Python or Spark to interactively explore and visualize data, prepare data for analytics and ML, and train ML models. Choose the plus sign and for Notebook , choose Python 3.

Big Data Architect 159

Big Data Architect Big Data ML Generative AI 159

AWS Machine Learning Blog

JULY 6, 2023

The open-source Custom Connector SDK enables the development of a private, shared, or public connector using Python or Java. SaaS platform SDK – If the SaaS platform has an SDK (Software Development Kit), such as a Python SDK, this can be used to access data directly from a SageMaker notebook.

ML 97

ML Data Scientist Metadata Python 97

The MLOps Blog

JANUARY 26, 2024

A feature store typically comprises a feature repository, a feature serving layer, and a metadata store. The metadata store manages the metadata associated with each feature, such as its origin and transformations. All of them are written in Python. It can also transform incoming data on the fly.

Machine Learning 72

Machine Learning Metadata ML Python 72

AWS Machine Learning Blog

JUNE 25, 2024

When the automated content processing steps are complete, you can use the output for downstream tasks, such as to invoke different components in a customer service backend application, or to insert the generated tags into metadata of each document for product recommendation.

Automation 124

Automation Prompt Engineering Prompt Engineer Categorization 124

The MLOps Blog

MARCH 15, 2023

In the case of our CI/CD-MLOPs system, we stored the model versions and metadata in the data storage services offered by AWS i.e ” Hence the very first thing to do is to make sure that the data being used is of high quality and that any errors or anomalies are detected and corrected before proceeding with ETL and data sourcing.

ETL 52

ETL Data Drift Machine Learning ML 52

Heartbeat

NOVEMBER 6, 2023

You also learned how to build an Extract Transform Load (ETL) pipeline and discovered the automation capabilities of Apache Airflow for ETL pipelines. You might need to extract the weather and metadata information about the location, after which you will combine both for transformation. This type of execution is shown below.

ETL 52

ETL Python Metadata Deep Learning 52

ODSC - Open Data Science

OCTOBER 18, 2024

Audit existing data assets Inventory internal datasets, ETL capabilities, past analytical initiatives, and available skill sets. Prioritize libraries with strong community support like Python and R. Applying consistent semantic standards and metadata makes governance scalable.

Data Science 52

Data Science ETL Data Scientist Data Quality 52

Applied Data Science

MAY 6, 2021

To keep myself sane, I use Airflow to automate tasks with simple, reusable pieces of code for frequently repeated elements of projects, for example: Web scraping ETL Database management Feature building and data validation And much more! Note that we can use the core python package datetime to help us define our DAGs.

Data Scientist 52

Data Scientist Automation Python Data Science 52

AWS Machine Learning Blog

APRIL 16, 2024

Jupyter notebooks can differentiate between SQL and Python code using the %%sm_sql magic command, which must be placed at the top of any cell that contains SQL code. This command signals to JupyterLab that the following instructions are SQL commands rather than Python code.

Data Scientist 117

Data Scientist Generative AI Machine Learning Auto-complete 117

The MLOps Blog

DECEMBER 7, 2022

And that’s when what usually happens, happened: We came for the ML models, we stayed for the ETLs. But even when the ETLs were well thought out, they were a bit “outdated” in their approach. ETL Pipeline ETL Pipeline | Source: Author The pipeline is triggered by Eventbridge , and can be done either manually or by cron.

ML 52

ML ETL Data Scientist Automation 52

The MLOps Blog

JANUARY 23, 2023

More useful resources about DVC: Versioning data and models Data version control with Python and DVC DVCorg YouTube DVC data version control cheatsheet At this point, one question arises; why use DVC instead of Git? It provides options for tracking, organizing, and storing metadata from machine learning experiments. neptune.ai

ML 52

ML Machine Learning Metadata Data Scientist 52

The MLOps Blog

AUGUST 3, 2023

At a high level, we are trying to make machine learning initiatives more human capital efficient by enabling teams to more easily get to production and maintain their model pipelines, ETLs, or workflows. You could almost think of Hamilton as DBT for Python functions. It gives a very opinionary way of writing Python. Stefan: Yep!

ML 52

ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

MAY 31, 2023

This is the ETL (Extract, Transform, and Load) layer that combines data from multiple sources, cleans noise from the data, organizes raw data, and prepares for model training. JupyterLabs has been one of the most popular interactive tools for ML development with Python. Might be useful With neptune.ai

ML 59

ML Algorithm Data Drift Machine Learning 59

Flipboard

NOVEMBER 24, 2023

This use case highlights how large language models (LLMs) are able to become a translator between human languages (English, Spanish, Arabic, and more) and machine interpretable languages (Python, Java, Scala, SQL, and so on) along with sophisticated internal reasoning.

ETL 157

ETL Prompt Engineering Prompt Engineer Generative AI 157

IBM Journey to AI blog

OCTOBER 30, 2024

The enhanced metadata supports the matching categories to internal controls and other relevant policy and governance datasets. Notably, the prompt text, model references, and prompt engineering parameters are meticulously formatted as Python code within notebooks, allowing for seamless programmable interaction. Furthermore, watsonx.ai

Prompt Engineering 238

Prompt Engineering Prompt Engineer ETL Machine Learning 238